Background and Aim: Nowadays, there are increasing amounts of data in various fields, which calls for special methods for management and extraction of information. Therefore, use of expert systems in different fields in particular medicine has attracted the attention of many investigators. Prediction of diseases such as heart attack is also a complex issue for which selection of major risk factors and obtaining correct results have been considered essential.

Material and Methods: In this study, using fuzzy system, a model was designed which works based on medical knowledge and discerning comparison. In this system the criteria used for the diagnosis heart attack are introduced into the system. Then theses criteria will be used for the risk factors in order to predict presence or absence of heart attack. In order to increase efficiency and accuracy of the system, the influence of the more important risk factors have received higher values. The proposed algorithm was used for the data collected from 1000 heart attack cases and patients without heart disease by using fuzzy systems in Tohid Hospital in Sanandaj.

Results: The proposed algorithm could predict heart disease with 98% accuracy in the subjects predisposed to heart attack. Another advantage of this method is its high efficiency in the absence of important diagnostic methods, such as exercise testing.

Conclusion: The proposed algorithm can accurately identify patients with heart disease. Risk factors such as age, blood pressure, unhealthy fat, smoking, family history and gender have significant impacts on the development of heart disease, Therefore, designing interventional programs by medical centers and providing information by mass media can be useful for prevention of heart attack.

Keywords: Prediction of heart attack, Fuzzy systems, Fuzzy inference engine, Risk factors.

Received: Feb 6, 2016      Accepted: Jun 21, 2016